Visuomotor adaptation across the lifespan

Being able to adapt our movements to changing circumstances allows people to maintain performance across a wide range of tasks throughout life, but it is unclear whether visuomotor learning abilities are fully developed in young children and, if so, whether they remain stable in the elderly. There is limited evidence of changes in motor adaptation ability throughout life, and the findings are inconsistent. Therefore, our goal was to compare visuomotor learning abilities throughout the lifespan. We used a shorter, gamified experimental task and collected data from participants in 5 age groups. Young children (M = 7 years), older children (M = 11 years), young adults (M = 20 years), adults (M = 40 years) and older adults (M = 67 years) adapted to a 45° visuomotor rotation in a centre-out reaching task. Across measures of rate of adaptation, extent of learning, rate of unlearning, generalization, and savings, we found that all groups performed similarly. That is, at least for short bouts of gamified learning, children and older adults perform just as well as young adults.


Learning rates
In order to assess the speed at which participants in each age category adept, de-adapt and re-adapt, we fit 3 exponential functions to the learning (or unlearning) curves.For the case of adaptation and relearning, we use this equation: Where x is the predicted set of reach deviations, t is a sequence of trials with the first trial having number 0, and two free parameters: a is an asymptote and l is a learning rate.For washout (or unlearning), we fit this equation: Which works almost the same way, but here we have a starting level s, and a retention rate r, that is used to predict the reach deviations x over time t.
For the initial phase we baseline the data using the last 9 reach deviations in the aligned phase.For relearning, we see that participants didn't decay back to zero, so we use the predicted value for the trial after the last washout trial as a reference level.This also means that asymptotes are relative to that level, and not relative to zero.
We used bootstrapping (1k samples of the same number of participants as are in each group, with replacement, from all participants in each group) to obtain 95% confidence intervals that could be used as descriptive statistics for comparisons between groups or conditions (these do not account for multiple comparisons though).
In order for the bootstrapped values in the relearning phase to be based on the correct starting levels, the washout and relearning fits were done on the same random samples.The initial learning rates were bootstrapped separately however.
We evaluated the quality of fit using the mean squared error (MSE) between the predictions from the fit and the actual data.In each fit, we first did a grid search on 11x11=121 combinations of parameter values spread evenly through the parameter space, calculating the MSE for those combinations.We used the 10 parameter combinations with the lowest MSE's as starting points for a quasi-Newton optimization algorithm with box constraints that further reduced the MSE (the rates were restricted to the range [0,1] and the asymptotes and starting value to the range [0,max(data)]).The set of parameters yielding the lowest MSE was used as the optimal fit.This was repeated for each of the 1k samples of participants, to get the confidence interval for each parameter.We got the central value of each parameter by fitting the function to the data from all participants.
For each fit, we only used the data for the 45 degree targets, and averaged across participants to get a single time course to fit.
The central values and 95% confidence intervals of the mean for each parameter can be found in table 1 below.1. Exponential function fits with parameter confidence intervals.In columns are the different age groups, from youngest to oldest.In rows we find the parameters.The top two rows present values for exponential fits to reach deviations in the initial adaptation block.The next two rows present values for parameters describing exponential decay during the washout/generalization phase.The last two rows represent parameter values fitting the time course of reach deviations in the relearning phase, including the "base level", i.e. the reach deviation predicted by the fit to the washout phase on the first trial of the retention phase.In each cell, we first see the value obtained from a fit to data from the full group of participants, followed by the 95% confidence interval in square brackets, obtained from a bootstrapping procedure.